Forest management often requires estimates to be made of the number of trees that are growing in a stand or other region of interest. In the past, such estimates were made by sending survey crews into the forest area to obtain sample data. From the sample data, the number of trees or other information could then be made by extrapolating the sample data to the size of the forest in question. While statistical sampling generally works well, it is often prohibitively expensive or logistically impractical to send survey crews into remote areas of the forest to obtain good sample data.
As an alternative to using human survey crews to collect the sample data, remote sensing techniques are being increasingly used to inventory forest areas. One such remote sensing technology used to survey a forest is LiDAR (light detection and ranging). With a LiDAR sensing system, a laser transmission and detection unit is carried by an aircraft over a number of overlapping flight paths that extend above a forest canopy. The LiDAR sensing system operates to transmit laser pulses in a repeating arc such that the pulses can be detected as they are reflected from the forest canopy, the ground or other natural or man made objects as the aircraft flies along. For each detected laser pulse, the LiDAR sensing system records the angle at which the pulse was received, the round trip time of flight of the pulse and the intensity of the detected pulse. The LiDAR sensing system also receives data from a GPS system and the altimeter of the aircraft so that a three-dimensional geographic location for each detected laser pulse can be determined. Data representing the three-dimensional location of each detected pulse are stored on a computer-readable medium (e.g. hard drive) in the LiDAR sensing system for later analysis with a computer.
The three-dimensional LiDAR data represents a surface map of a forest canopy. However it is often difficult to identify individual trees in the LiDAR data. As a result, a number of statistical approaches have been proposed to identify groups of LiDAR data points that represent individual trees. While such methods have met with varying degrees of success, there is a need for an improved, less computationally complex, method of identifying individual trees in LiDAR data.